Transmission of moving pictures in real-time is employed in several applications like e.g. video conferencing, net meetings, TV broadcasting and video telephony.
However, representing moving pictures requires bulk information as digital video which is typically described by representing each pixel in a picture with 8 bits (1 Byte). Such uncompressed video data results in large bit volumes, and cannot be transferred over conventional communication networks and transmission lines in real time due to limited bandwidth.
Thus, enabling real time video transmission requires a large extent of data compression. Data compression may, however, compromise with picture quality. Therefore, great efforts have been made to develop compression techniques allowing real time transmission of high quality video over bandwidth limited data connections.
In video compression systems, the main goal is to represent the video information with as little capacity as possible. Capacity is defined with bits, either as a constant value or as bits/time unit. In both cases, the main goal is to reduce the number of bits.
The most common video coding method is described in the MPEG* and H.26* standards. The video data undergo four main processes before transmission, namely prediction, transformation, quantization and entropy coding.
The prediction process significantly reduces the amount of bits required for each picture in a video sequence to be transferred. It takes advantage of the similarity of parts of the sequence with other parts of the sequence. Since the predictor part is known to both encoder and decoder, only the difference has to be transferred. This difference typically requires much less capacity for its representation. The prediction is mainly based on vectors representing movements. The prediction process is typically performed on square block sizes (e.g. 16×16 pixels). Note that in some cases, predictions of pixels based on the adjacent pixels in the same picture rather than pixels of preceding pictures are used. This is referred to as intra prediction, as opposed to inter prediction. Consequently, when the pixels in a block are coded by means of intra prediction, the block is said to be an intra coded.
The residual represented as a block of data (e.g. 4×4 pixels) still contains internal correlation. A well-known method of taking advantage of this is to perform a two dimensional block transform. In H.263 an 8×8 Discrete Cosine Transform (DCT) is used, whereas H.264 uses a 4×4 integer type transform. This transforms 4×4 pixels into 4×4 transform coefficients and they can usually be represented by fewer bits than the pixel representation. Transform of a 4×4 array of pixels with internal correlation will probably result in a 4×4 block of transform coefficients with much fewer non-zero values than the original 4×4 pixel block.
Direct representation of the transform coefficients is still too costly for many applications. A quantization process is carried out for a further reduction of the data representation. Hence the transform coefficients undergo quantization. A simple version of quantisation is to divide parameter values by a number—resulting in a smaller number that may be represented by fewer bits. It should be mentioned that this quantization process has as a result that the reconstructed video sequence is somewhat different from the uncompressed sequence. This phenomenon is referred to as “lossy coding”. The outcome from the quantisation part is referred to as quantized transform coefficients.
Entropy coding implies lossless representation of different types of parameters such as overhead data or system description, prediction data (typically motion vectors), and quantized transform coefficients from the quantisation process. The latter typically represent the largest bit consumption.
The coding is performed on block wise parts of the video picture. A macro block consists of several sub blocks for luminance (luma) as well as for chrominance (chroma).
The present video standards H.261, H.262, H.263, H.264/AVC, MPEG1, MPEG2, MPEG4 all use blockbased coding. This means blockbased prediction from previously encoded and decoded pictures as well as blockbased coding of residual signal.
Blockbased coding/decoding has proven to be very efficient. However, one of the drawbacks is that the reconstructed image may have visible artifacts corresponding to the blocks used for prediction and residual signal coding. This phenomenon is usually referred to as blocking or blocking artifacts.
One way of reducing the artifacts known in prior art is to add a post processing filter between the decoder and the display unit at the receiver, an example of which is shown in FIG. 1. The filtering operation takes place right before the presentation of the picture. It is therefore a pure decoder/display issue that is unrelated to what the encoder does. In alternative solution, as shown in FIG. 2, the filter is integrated in the coding loop. This is a more powerful approach, and is the preferred solution in the specification ITU-T Rec. H.264|ISO/IEC 14496-10 AVC, even if it implies that both encoder and decoder need to do the same operations to avoid drift in the reconstructed pictures. However, the integrated solution is quite processor consuming i.a. as it requires a test procedure for each pixel line crossing the block edges to be smoothed.